The collection and storage of a large number of medical images is currently carried out by a number of systems. The medical images can be collected by a variety of techniques, such as nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and x-rays. One system for collecting a large number of medical images of a human body is disclosed U.S. Pat. Nos. 5,311,131 and 5,818,231 to Smith. These patents describe an MRI apparatus and method for collecting a large number of medical images in various data sets. The data are organized and manipulated in order to provide visual images to be read by medical personnel to perform a diagnosis.
One of the problems in reading a large number of images is for the medical personnel to understand the relationship of the images to each other while performing the reading. Another difficult task is interpreting the medical significance of various features that are shown in the individual images. Being able to correlate the images with respect to each other is extremely important in deriving the most accurate medical diagnosis from the images and in setting forth a standard of treatment for the respective patient. Unfortunately, such a coordination of multiple images with respect to each other is extremely difficult and even highly trained medical personnel, such as experienced radiologists, have extreme difficulty in consistently and properly interpreting a series of medical images so that a treatment regime can be instituted that best fits the patient's current medical condition.
Another problem encountered by medical personnel today is the large amount of data and numerous images that are obtained from current medical imaging devices. The number of images collected in a standard scan is usually in excess of 100 and very frequently numbers in the many hundreds. In order for medical personnel to properly review each image takes a great deal of time, and with the many images that current medical technology provides, a great amount of time is required to thoroughly examine all the data.
These problems are compounded by the fact that there is a wide variety of literature and research that provide different approaches as to how to analyze the data. Different doctors use different analytical criteria to determine whether tissue shown in images is malignant or benign, for instance. Indeed, it is universally accepted that doctors will often have different opinions as to the diagnosis and treatment regimen for a particular patient. Hence, patients often obtain “second opinions” as a means for comparing doctors' diagnosis and suggested treatment regimens.
However, despite the large amount of available data and despite the fact that doctors often take different evaluative approaches, existing technology has not been able to adapt to these environments. For example, when generating and displaying images for a doctor to review, the imaging device (sometimes referred to as a “workstation”) uses a standard set of configuration settings to sort, classify, or otherwise process the images. The configuration settings are applied universally to all patients and to all images.
While this uniformity of processing provides simplicity, it is non-ideal. Human tissue behavior will vary greatly (or subtly) from one patient to another during the image acquisition process. Moreover, diseases will often act differently on different types of tissue, thereby producing differences in images. Thus, if certain analytical criteria is used to classify tissue images of a particular patient and is suggestive of the presence of cancerous tissue, that conclusion may not necessarily apply to tissue of another patient that is processed using the same analytical criteria. Misdiagnosis may result.
Moreover, using a standard set of parameters to universally process all images forces doctors to conform their analysis and conclusions to be consistent with the standard parameters. This can result in misdiagnosis, particularly if a certain patient has a tissue condition that is inconsistent with tissue behavior on which the standard parameters are based. Alternatively, doctors may be forced to constantly adjust their individual independent analysis to account for the standardization of the images. This can create a certain amount of guesswork and approximation that ultimately may be detrimental to the patient.
A possible solution may be to allow doctors to perform adjustments to the preset criteria via the workstation. However, some doctors are not as computer-savvy as other doctors, and requiring doctors to repeatedly perform this reconfiguration of settings for all images, each and every time they log onto a workstation, is too burdensome of a task to be beneficial.